Which tool is best suited for data transformation in Fabric when dealing with large-scale data that will continue to grow?

Which tool is best suited for data transformation in Fabric when dealing with large-scale data that will continue to grow?

• A. Dataflows (Gen2)
• B. Pipelines
• C. Notebooks
• D. SQL Analytics

Answer: (C)

Transforming growing, large-scale data benefits from a flexible, code-driven approach that can scale out as the data expands. Notebooks provide a code-first environment where you can implement, test, and iterate complex transformations using Python, SQL, or Spark. In Fabric, notebooks run on scalable compute, allowing distributed processing with libraries like PySpark or Dask, so the same transformation logic can handle increasing data volumes without being restricted by a GUI or a fixed workflow. Notebooks also offer reproducibility and modularity: you can parameterize inputs, manage dependencies with environment specs, and compose reusable functions or pieces of logic. This makes it easier to evolve transformation pipelines as data grows, without rewriting large parts of your workflow. While declarative dataflows are great for standard ETL with a visual interface, they’re less suited to rapidly evolving, advanced transformations at scale. Pipelines focus on orchestration rather than the transformation logic itself, and SQL Analytics centers on SQL-based analysis rather than flexible, scalable data transformation.

Transforming growing, large-scale data benefits from a flexible, code-driven approach that can scale out as the data expands. Notebooks provide a code-first environment where you can implement, test, and iterate complex transformations using Python, SQL, or Spark. In Fabric, notebooks run on scalable compute, allowing distributed processing with libraries like PySpark or Dask, so the same transformation logic can handle increasing data volumes without being restricted by a GUI or a fixed workflow.

Notebooks also offer reproducibility and modularity: you can parameterize inputs, manage dependencies with environment specs, and compose reusable functions or pieces of logic. This makes it easier to evolve transformation pipelines as data grows, without rewriting large parts of your workflow. While declarative dataflows are great for standard ETL with a visual interface, they’re less suited to rapidly evolving, advanced transformations at scale. Pipelines focus on orchestration rather than the transformation logic itself, and SQL Analytics centers on SQL-based analysis rather than flexible, scalable data transformation.